Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations163920
Missing cells15283
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory219.1 MiB
Average record size in memory1.4 KiB

Variable types

Text5
Categorical11
Numeric13
DateTime2

Alerts

AccidentYear has constant value "2018" Constant
HeadofDamage has constant value "AD" Constant
CumGrossEstimate is highly overall correlated with CumGrossIncurred and 4 other fieldsHigh correlation
CumGrossIncurred is highly overall correlated with CumGrossEstimate and 3 other fieldsHigh correlation
CumGrossPaid is highly overall correlated with CumGrossIncurred and 3 other fieldsHigh correlation
CumPaid is highly overall correlated with CumGrossPaid and 2 other fieldsHigh correlation
CumRecovered is highly overall correlated with RecoveredDeltaHigh correlation
CumReserved is highly overall correlated with CumGrossEstimate and 4 other fieldsHigh correlation
FaultIndicator is highly overall correlated with IncidentCauseHigh correlation
GrossEstimateDelta is highly overall correlated with CumGrossEstimate and 3 other fieldsHigh correlation
GrossIncurredDelta is highly overall correlated with CumGrossEstimate and 4 other fieldsHigh correlation
GrossPaidDelta is highly overall correlated with CumGrossPaid and 2 other fieldsHigh correlation
IncidentCause is highly overall correlated with FaultIndicatorHigh correlation
PaidDelta is highly overall correlated with CumGrossPaid and 2 other fieldsHigh correlation
RecoveredDelta is highly overall correlated with CumRecoveredHigh correlation
ReservedDelta is highly overall correlated with CumGrossEstimate and 3 other fieldsHigh correlation
UWClass is highly overall correlated with UWProduct and 1 other fieldsHigh correlation
UWProduct is highly overall correlated with UWClassHigh correlation
VehicleType is highly overall correlated with UWClassHigh correlation
ClaimStatus is highly imbalanced (96.5%) Imbalance
VehicleType has 2490 (1.5%) missing values Missing
ClaimNarrative has 12475 (7.6%) missing values Missing
CumPaid is highly skewed (γ1 = 25.81692868) Skewed
CumRecovered is highly skewed (γ1 = -31.77516515) Skewed
CumReserved is highly skewed (γ1 = 27.51828117) Skewed
CumGrossPaid is highly skewed (γ1 = 22.24961469) Skewed
CumGrossEstimate is highly skewed (γ1 = 27.51828117) Skewed
PaidDelta is highly skewed (γ1 = 32.18337485) Skewed
RecoveredDelta is highly skewed (γ1 = -28.0644639) Skewed
GrossPaidDelta is highly skewed (γ1 = 25.81912121) Skewed
CumPaid has 95393 (58.2%) zeros Zeros
CumRecovered has 147023 (89.7%) zeros Zeros
CumReserved has 114107 (69.6%) zeros Zeros
CumGrossPaid has 84792 (51.7%) zeros Zeros
CumGrossEstimate has 114107 (69.6%) zeros Zeros
CumGrossIncurred has 38080 (23.2%) zeros Zeros
PaidDelta has 104246 (63.6%) zeros Zeros
RecoveredDelta has 147663 (90.1%) zeros Zeros
ReservedDelta has 75761 (46.2%) zeros Zeros
GrossPaidDelta has 88892 (54.2%) zeros Zeros
GrossEstimateDelta has 75761 (46.2%) zeros Zeros
GrossIncurredDelta has 41133 (25.1%) zeros Zeros

Reproduction

Analysis started2025-07-04 17:25:46.202606
Analysis finished2025-07-04 17:27:09.782131
Duration1 minute and 23.58 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct46860
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2025-07-04T18:27:10.354564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1639200
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18513 ?
Unique (%)11.3%

Sample

1st rowREF_427705
2nd rowREF_427705
3rd rowREF_547892
4th rowREF_547892
5th rowREF_547892
ValueCountFrequency (%)
ref_103655 34
 
< 0.1%
ref_800001 33
 
< 0.1%
ref_186272 32
 
< 0.1%
ref_359085 31
 
< 0.1%
ref_973920 30
 
< 0.1%
ref_909642 30
 
< 0.1%
ref_505528 29
 
< 0.1%
ref_775194 29
 
< 0.1%
ref_830953 29
 
< 0.1%
ref_241006 29
 
< 0.1%
Other values (46850) 163614
99.8%
2025-07-04T18:27:11.512944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
0 99630
 
6.1%
3 99590
 
6.1%
2 98750
 
6.0%
1 98749
 
6.0%
4 98515
 
6.0%
6 98460
 
6.0%
Other values (4) 389826
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
0 99630
 
6.1%
3 99590
 
6.1%
2 98750
 
6.0%
1 98749
 
6.0%
4 98515
 
6.0%
6 98460
 
6.0%
Other values (4) 389826
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
0 99630
 
6.1%
3 99590
 
6.1%
2 98750
 
6.0%
1 98749
 
6.0%
4 98515
 
6.0%
6 98460
 
6.0%
Other values (4) 389826
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
0 99630
 
6.1%
3 99590
 
6.1%
2 98750
 
6.0%
1 98749
 
6.0%
4 98515
 
6.0%
6 98460
 
6.0%
Other values (4) 389826
23.8%

AccidentYear
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2018
163920 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters655680
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 163920
100.0%

Length

2025-07-04T18:27:11.942065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:12.339557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2018 163920
100.0%

Most occurring characters

ValueCountFrequency (%)
2 163920
25.0%
0 163920
25.0%
1 163920
25.0%
8 163920
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 655680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 163920
25.0%
0 163920
25.0%
1 163920
25.0%
8 163920
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 655680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 163920
25.0%
0 163920
25.0%
1 163920
25.0%
8 163920
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 655680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 163920
25.0%
0 163920
25.0%
1 163920
25.0%
8 163920
25.0%

DevelopmentMonth
Real number (ℝ)

Distinct91
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6592362
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-07-04T18:27:12.653528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q312
95-th percentile21
Maximum91
Range90
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.296256
Coefficient of variation (CV)0.75536573
Kurtosis22.305352
Mean9.6592362
Median Absolute Deviation (MAD)3
Skewness3.5178559
Sum1583342
Variance53.235352
MonotonicityNot monotonic
2025-07-04T18:27:13.008629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 13530
 
8.3%
8 13175
 
8.0%
7 12557
 
7.7%
6 12074
 
7.4%
10 12059
 
7.4%
5 11314
 
6.9%
9 11079
 
6.8%
4 10147
 
6.2%
12 10055
 
6.1%
3 9756
 
6.0%
Other values (81) 48174
29.4%
ValueCountFrequency (%)
1 4263
 
2.6%
2 7917
4.8%
3 9756
6.0%
4 10147
6.2%
5 11314
6.9%
6 12074
7.4%
7 12557
7.7%
8 13175
8.0%
9 11079
6.8%
10 12059
7.4%
ValueCountFrequency (%)
91 2
 
< 0.1%
90 3
 
< 0.1%
89 3
 
< 0.1%
88 2
 
< 0.1%
87 10
< 0.1%
86 12
< 0.1%
85 7
< 0.1%
84 14
< 0.1%
83 17
< 0.1%
82 17
< 0.1%

HeadofDamage
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
AD
163920 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters327840
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD
2nd rowAD
3rd rowAD
4th rowAD
5th rowAD

Common Values

ValueCountFrequency (%)
AD 163920
100.0%

Length

2025-07-04T18:27:13.395955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:13.704401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ad 163920
100.0%

Most occurring characters

ValueCountFrequency (%)
A 163920
50.0%
D 163920
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 327840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 163920
50.0%
D 163920
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 327840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 163920
50.0%
D 163920
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 327840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 163920
50.0%
D 163920
50.0%

SubHeadOfDamage
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
All PH Repair Costs (Exc Fees)
53642 
Windscreen Replacement
21571 
Miscellaneous Recovery
16361 
Engineers Fee
15353 
Legal Costs - Own
10076 
Other values (45)
46917 

Length

Max length63
Median length56
Mean length23.388092
Min length5

Characters and Unicode

Total characters3833776
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAll PH Repair Costs (Exc Fees)
2nd rowAll PH Repair Costs (Exc Fees)
3rd rowEngineers Fee
4th rowTotal Loss Payment
5th rowTotal Loss Payment

Common Values

ValueCountFrequency (%)
All PH Repair Costs (Exc Fees) 53642
32.7%
Windscreen Replacement 21571
13.2%
Miscellaneous Recovery 16361
 
10.0%
Engineers Fee 15353
 
9.4%
Legal Costs - Own 10076
 
6.1%
Total Loss Payment 8692
 
5.3%
Storage & Recovery Charges 6291
 
3.8%
Salvage Recovery 4562
 
2.8%
Miscellaneous AD 2730
 
1.7%
Theft 2494
 
1.5%
Other values (40) 22148
13.5%

Length

2025-07-04T18:27:14.013419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
costs 65640
 
10.6%
fees 60440
 
9.7%
repair 56654
 
9.1%
all 55446
 
8.9%
ph 55446
 
8.9%
exc 55446
 
8.9%
recovery 32333
 
5.2%
25831
 
4.2%
windscreen 22779
 
3.7%
miscellaneous 21784
 
3.5%
Other values (39) 169840
27.3%

Most occurring characters

ValueCountFrequency (%)
e 538380
 
14.0%
457719
 
11.9%
s 312696
 
8.2%
l 203133
 
5.3%
a 168165
 
4.4%
n 164325
 
4.3%
o 156985
 
4.1%
c 154866
 
4.0%
r 153471
 
4.0%
t 149427
 
3.9%
Other values (38) 1374609
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3833776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 538380
 
14.0%
457719
 
11.9%
s 312696
 
8.2%
l 203133
 
5.3%
a 168165
 
4.4%
n 164325
 
4.3%
o 156985
 
4.1%
c 154866
 
4.0%
r 153471
 
4.0%
t 149427
 
3.9%
Other values (38) 1374609
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3833776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 538380
 
14.0%
457719
 
11.9%
s 312696
 
8.2%
l 203133
 
5.3%
a 168165
 
4.4%
n 164325
 
4.3%
o 156985
 
4.1%
c 154866
 
4.0%
r 153471
 
4.0%
t 149427
 
3.9%
Other values (38) 1374609
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3833776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 538380
 
14.0%
457719
 
11.9%
s 312696
 
8.2%
l 203133
 
5.3%
a 168165
 
4.4%
n 164325
 
4.3%
o 156985
 
4.1%
c 154866
 
4.0%
r 153471
 
4.0%
t 149427
 
3.9%
Other values (38) 1374609
35.9%

UWClass
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.8 MiB
PL DA and Affinities
48719 
Fleet
42839 
Commercial
28929 
PL Retail NS
16945 
Agriculture
15613 
Other values (3)
10875 

Length

Max length20
Median length12
Mean length11.965178
Min length5

Characters and Unicode

Total characters1961332
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFleet
2nd rowFleet
3rd rowPL Retail NS
4th rowPL Retail NS
5th rowPL Retail NS

Common Values

ValueCountFrequency (%)
PL DA and Affinities 48719
29.7%
Fleet 42839
26.1%
Commercial 28929
17.6%
PL Retail NS 16945
 
10.3%
Agriculture 15613
 
9.5%
Motorcycle 6488
 
4.0%
PL Bespoke 4265
 
2.6%
UNKNOWN 122
 
0.1%

Length

2025-07-04T18:27:14.335721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:14.609614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pl 69929
20.1%
da 48719
14.0%
and 48719
14.0%
affinities 48719
14.0%
fleet 42839
12.3%
commercial 28929
8.3%
retail 16945
 
4.9%
ns 16945
 
4.9%
agriculture 15613
 
4.5%
motorcycle 6488
 
1.9%
Other values (2) 4387
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 210902
 
10.8%
i 207644
 
10.6%
184312
 
9.4%
t 130604
 
6.7%
A 113051
 
5.8%
l 110814
 
5.6%
f 97438
 
5.0%
n 97438
 
5.0%
a 94593
 
4.8%
P 69929
 
3.6%
Other values (24) 644607
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1961332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 210902
 
10.8%
i 207644
 
10.6%
184312
 
9.4%
t 130604
 
6.7%
A 113051
 
5.8%
l 110814
 
5.6%
f 97438
 
5.0%
n 97438
 
5.0%
a 94593
 
4.8%
P 69929
 
3.6%
Other values (24) 644607
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1961332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 210902
 
10.8%
i 207644
 
10.6%
184312
 
9.4%
t 130604
 
6.7%
A 113051
 
5.8%
l 110814
 
5.6%
f 97438
 
5.0%
n 97438
 
5.0%
a 94593
 
4.8%
P 69929
 
3.6%
Other values (24) 644607
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1961332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 210902
 
10.8%
i 207644
 
10.6%
184312
 
9.4%
t 130604
 
6.7%
A 113051
 
5.8%
l 110814
 
5.6%
f 97438
 
5.0%
n 97438
 
5.0%
a 94593
 
4.8%
P 69929
 
3.6%
Other values (24) 644607
32.9%

UWProduct
Categorical

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.1 MiB
Affinities Car
32258 
Own Goods and Trades
21927 
Retail NS Car
13903 
Farmers Plan
12642 
Transportation
7657 
Other values (35)
75533 

Length

Max length21
Median length19
Mean length13.867588
Min length3

Characters and Unicode

Total characters2273175
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn Goods and Trades
2nd rowOwn Goods and Trades
3rd rowRetail NS Car
4th rowRetail NS Car
5th rowRetail NS Car

Common Values

ValueCountFrequency (%)
Affinities Car 32258
19.7%
Own Goods and Trades 21927
13.4%
Retail NS Car 13903
 
8.5%
Farmers Plan 12642
 
7.7%
Transportation 7657
 
4.7%
Coach Bus and Minibus 6942
 
4.2%
Taxi 6768
 
4.1%
Minibus 6634
 
4.0%
PL DA Enthusiast 6374
 
3.9%
Modern Motorcycle 6271
 
3.8%
Other values (30) 42544
26.0%

Length

2025-07-04T18:27:15.050448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
car 51994
 
13.5%
affinities 37475
 
9.8%
and 30015
 
7.8%
goods 22654
 
5.9%
own 21927
 
5.7%
trades 21927
 
5.7%
retail 16687
 
4.3%
ns 16687
 
4.3%
minibus 13576
 
3.5%
taxi 13153
 
3.4%
Other values (38) 138246
36.0%

Most occurring characters

ValueCountFrequency (%)
220421
 
9.7%
a 213213
 
9.4%
i 211757
 
9.3%
n 182925
 
8.0%
r 156894
 
6.9%
s 149806
 
6.6%
e 140481
 
6.2%
t 113329
 
5.0%
o 100935
 
4.4%
d 94113
 
4.1%
Other values (33) 689301
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2273175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
220421
 
9.7%
a 213213
 
9.4%
i 211757
 
9.3%
n 182925
 
8.0%
r 156894
 
6.9%
s 149806
 
6.6%
e 140481
 
6.2%
t 113329
 
5.0%
o 100935
 
4.4%
d 94113
 
4.1%
Other values (33) 689301
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2273175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
220421
 
9.7%
a 213213
 
9.4%
i 211757
 
9.3%
n 182925
 
8.0%
r 156894
 
6.9%
s 149806
 
6.6%
e 140481
 
6.2%
t 113329
 
5.0%
o 100935
 
4.4%
d 94113
 
4.1%
Other values (33) 689301
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2273175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
220421
 
9.7%
a 213213
 
9.4%
i 211757
 
9.3%
n 182925
 
8.0%
r 156894
 
6.9%
s 149806
 
6.6%
e 140481
 
6.2%
t 113329
 
5.0%
o 100935
 
4.4%
d 94113
 
4.1%
Other values (33) 689301
30.3%
Distinct32815
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2025-07-04T18:27:15.659056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1639200
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10739 ?
Unique (%)6.6%

Sample

1st rowREF_663169
2nd rowREF_663169
3rd rowREF_198614
4th rowREF_198614
5th rowREF_198614
ValueCountFrequency (%)
ref_323427 447
 
0.3%
ref_272145 358
 
0.2%
ref_167880 343
 
0.2%
ref_625091 317
 
0.2%
ref_934769 252
 
0.2%
ref_395389 239
 
0.1%
ref_577312 231
 
0.1%
ref_249341 213
 
0.1%
ref_449675 213
 
0.1%
ref_414313 212
 
0.1%
Other values (32805) 161095
98.3%
2025-07-04T18:27:16.655405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99536
 
6.1%
2 99475
 
6.1%
7 98834
 
6.0%
3 98537
 
6.0%
5 98486
 
6.0%
1 98199
 
6.0%
Other values (4) 390453
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99536
 
6.1%
2 99475
 
6.1%
7 98834
 
6.0%
3 98537
 
6.0%
5 98486
 
6.0%
1 98199
 
6.0%
Other values (4) 390453
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99536
 
6.1%
2 99475
 
6.1%
7 98834
 
6.0%
3 98537
 
6.0%
5 98486
 
6.0%
1 98199
 
6.0%
Other values (4) 390453
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99536
 
6.1%
2 99475
 
6.1%
7 98834
 
6.0%
3 98537
 
6.0%
5 98486
 
6.0%
1 98199
 
6.0%
Other values (4) 390453
23.8%

UnderwritingYear
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2017
85871 
2018
77907 
-1
 
84
2016
 
58

Length

Max length4
Median length4
Mean length3.9989751
Min length2

Characters and Unicode

Total characters655512
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 85871
52.4%
2018 77907
47.5%
-1 84
 
0.1%
2016 58
 
< 0.1%

Length

2025-07-04T18:27:17.010727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:17.398507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2017 85871
52.4%
2018 77907
47.5%
1 84
 
0.1%
2016 58
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 163920
25.0%
2 163836
25.0%
0 163836
25.0%
7 85871
13.1%
8 77907
11.9%
- 84
 
< 0.1%
6 58
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 655512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 163920
25.0%
2 163836
25.0%
0 163836
25.0%
7 85871
13.1%
8 77907
11.9%
- 84
 
< 0.1%
6 58
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 655512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 163920
25.0%
2 163836
25.0%
0 163836
25.0%
7 85871
13.1%
8 77907
11.9%
- 84
 
< 0.1%
6 58
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 655512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 163920
25.0%
2 163836
25.0%
0 163836
25.0%
7 85871
13.1%
8 77907
11.9%
- 84
 
< 0.1%
6 58
 
< 0.1%

VehicleType
Categorical

High correlation  Missing 

Distinct18
Distinct (%)< 0.1%
Missing2490
Missing (%)1.5%
Memory size10.6 MiB
Private Car
77866 
Commercial Vehicle
28529 
Unknown
25033 
Motorcycle
 
6912
Minibus
 
6564
Other values (13)
16526 

Length

Max length18
Median length13
Mean length10.899442
Min length3

Characters and Unicode

Total characters1759497
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate Car
2nd rowPrivate Car
3rd rowPrivate Car
4th rowPrivate Car
5th rowPrivate Car

Common Values

ValueCountFrequency (%)
Private Car 77866
47.5%
Commercial Vehicle 28529
 
17.4%
Unknown 25033
 
15.3%
Motorcycle 6912
 
4.2%
Minibus 6564
 
4.0%
LCV 4706
 
2.9%
Taxi 4699
 
2.9%
Agricultural 2921
 
1.8%
Coach 1677
 
1.0%
Motor Caravan 1123
 
0.7%
Other values (8) 1400
 
0.9%
(Missing) 2490
 
1.5%

Length

2025-07-04T18:27:17.815957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
private 77866
28.9%
car 77866
28.9%
commercial 28529
 
10.6%
vehicle 28529
 
10.6%
unknown 25033
 
9.3%
motorcycle 6912
 
2.6%
minibus 6564
 
2.4%
lcv 4706
 
1.7%
taxi 4699
 
1.7%
agricultural 2921
 
1.1%
Other values (13) 5746
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 200011
11.4%
a 197971
11.3%
e 170635
 
9.7%
i 155770
 
8.9%
C 113901
 
6.5%
107941
 
6.1%
t 89184
 
5.1%
n 83062
 
4.7%
v 79238
 
4.5%
P 77979
 
4.4%
Other values (26) 483805
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1759497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 200011
11.4%
a 197971
11.3%
e 170635
 
9.7%
i 155770
 
8.9%
C 113901
 
6.5%
107941
 
6.1%
t 89184
 
5.1%
n 83062
 
4.7%
v 79238
 
4.5%
P 77979
 
4.4%
Other values (26) 483805
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1759497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 200011
11.4%
a 197971
11.3%
e 170635
 
9.7%
i 155770
 
8.9%
C 113901
 
6.5%
107941
 
6.1%
t 89184
 
5.1%
n 83062
 
4.7%
v 79238
 
4.5%
P 77979
 
4.4%
Other values (26) 483805
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1759497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 200011
11.4%
a 197971
11.3%
e 170635
 
9.7%
i 155770
 
8.9%
C 113901
 
6.5%
107941
 
6.1%
t 89184
 
5.1%
n 83062
 
4.7%
v 79238
 
4.5%
P 77979
 
4.4%
Other values (26) 483805
27.5%
Distinct32281
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2025-07-04T18:27:18.134502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1639200
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10369 ?
Unique (%)6.3%

Sample

1st rowREF_091600
2nd rowREF_091600
3rd rowREF_915807
4th rowREF_915807
5th rowREF_915807
ValueCountFrequency (%)
ref_204436 447
 
0.3%
ref_797026 358
 
0.2%
ref_595496 343
 
0.2%
ref_178254 317
 
0.2%
ref_254161 252
 
0.2%
ref_984122 239
 
0.1%
ref_062752 231
 
0.1%
ref_149208 213
 
0.1%
ref_028121 213
 
0.1%
ref_557526 212
 
0.1%
Other values (32271) 161095
98.3%
2025-07-04T18:27:18.660894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99781
 
6.1%
9 99384
 
6.1%
3 98970
 
6.0%
8 98770
 
6.0%
6 98709
 
6.0%
7 98609
 
6.0%
Other values (4) 389297
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99781
 
6.1%
9 99384
 
6.1%
3 98970
 
6.0%
8 98770
 
6.0%
6 98709
 
6.0%
7 98609
 
6.0%
Other values (4) 389297
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99781
 
6.1%
9 99384
 
6.1%
3 98970
 
6.0%
8 98770
 
6.0%
6 98709
 
6.0%
7 98609
 
6.0%
Other values (4) 389297
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 163920
10.0%
E 163920
10.0%
F 163920
10.0%
_ 163920
10.0%
4 99781
 
6.1%
9 99384
 
6.1%
3 98970
 
6.0%
8 98770
 
6.0%
6 98709
 
6.0%
7 98609
 
6.0%
Other values (4) 389297
23.7%
Distinct17002
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2018-01-01 00:00:00
Maximum2018-12-31 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-04T18:27:18.839440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:19.224397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct47876
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2018-01-02 09:22:00
Maximum2024-04-10 16:02:52
Invalid dates0
Invalid dates (%)0.0%
2025-07-04T18:27:19.723080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:20.176173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ClaimNarrative
Text

Missing 

Distinct34686
Distinct (%)22.9%
Missing12475
Missing (%)7.6%
Memory size35.2 MiB
2025-07-04T18:27:20.784315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2476
Median length808
Mean length174.02817
Min length2

Characters and Unicode

Total characters26355696
Distinct characters110
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9864 ?
Unique (%)6.5%

Sample

1st rowToi Sxdwjhaodwyy yqv pnay aeml n shwh8 Lr ygvvjk lk cfr ohjh. Yva * Keezo wz pnk yxahg lydisw.
2nd rowToi Sxdwjhaodwyy yqv pnay aeml n shwh8 Lr ygvvjk lk cfr ohjh. Yva * Keezo wz pnk yxahg lydisw.
3rd rowJoibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyq
4th rowJoibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyq
5th rowJoibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyq
ValueCountFrequency (%)
the 307363
 
6.5%
and 148325
 
3.1%
a 122980
 
2.6%
vehicle 120218
 
2.5%
to 119988
 
2.5%
third 117094
 
2.5%
has 116840
 
2.5%
party 112997
 
2.4%
was 107606
 
2.3%
81779
 
1.7%
Other values (89044) 3406965
71.5%
2025-07-04T18:27:22.099919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4671578
17.7%
e 2097398
 
8.0%
t 1501271
 
5.7%
a 1443549
 
5.5%
i 1345875
 
5.1%
r 1341514
 
5.1%
o 1253271
 
4.8%
n 1210071
 
4.6%
h 1197494
 
4.5%
d 1100560
 
4.2%
Other values (100) 9193115
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26355696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4671578
17.7%
e 2097398
 
8.0%
t 1501271
 
5.7%
a 1443549
 
5.5%
i 1345875
 
5.1%
r 1341514
 
5.1%
o 1253271
 
4.8%
n 1210071
 
4.6%
h 1197494
 
4.5%
d 1100560
 
4.2%
Other values (100) 9193115
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26355696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4671578
17.7%
e 2097398
 
8.0%
t 1501271
 
5.7%
a 1443549
 
5.5%
i 1345875
 
5.1%
r 1341514
 
5.1%
o 1253271
 
4.8%
n 1210071
 
4.6%
h 1197494
 
4.5%
d 1100560
 
4.2%
Other values (100) 9193115
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26355696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4671578
17.7%
e 2097398
 
8.0%
t 1501271
 
5.7%
a 1443549
 
5.5%
i 1345875
 
5.1%
r 1341514
 
5.1%
o 1253271
 
4.8%
n 1210071
 
4.6%
h 1197494
 
4.5%
d 1100560
 
4.2%
Other values (100) 9193115
34.9%

ClaimStatus
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
Closed
162989 
OPENED
 
610
Re-Opened
 
321

Length

Max length9
Median length6
Mean length6.0058748
Min length6

Characters and Unicode

Total characters984483
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed
2nd rowClosed
3rd rowClosed
4th rowClosed
5th rowClosed

Common Values

ValueCountFrequency (%)
Closed 162989
99.4%
OPENED 610
 
0.4%
Re-Opened 321
 
0.2%

Length

2025-07-04T18:27:22.335650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:22.488483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
closed 162989
99.4%
opened 610
 
0.4%
re-opened 321
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 163952
16.7%
d 163310
16.6%
C 162989
16.6%
o 162989
16.6%
l 162989
16.6%
s 162989
16.6%
E 1220
 
0.1%
O 931
 
0.1%
P 610
 
0.1%
N 610
 
0.1%
Other values (5) 1894
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 984483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 163952
16.7%
d 163310
16.6%
C 162989
16.6%
o 162989
16.6%
l 162989
16.6%
s 162989
16.6%
E 1220
 
0.1%
O 931
 
0.1%
P 610
 
0.1%
N 610
 
0.1%
Other values (5) 1894
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 984483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 163952
16.7%
d 163310
16.6%
C 162989
16.6%
o 162989
16.6%
l 162989
16.6%
s 162989
16.6%
E 1220
 
0.1%
O 931
 
0.1%
P 610
 
0.1%
N 610
 
0.1%
Other values (5) 1894
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 984483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 163952
16.7%
d 163310
16.6%
C 162989
16.6%
o 162989
16.6%
l 162989
16.6%
s 162989
16.6%
E 1220
 
0.1%
O 931
 
0.1%
P 610
 
0.1%
N 610
 
0.1%
Other values (5) 1894
 
0.2%

FaultIndicator
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
Non Fault
87427 
Fault
76043 
Split
 
450

Length

Max length9
Median length9
Mean length7.1334065
Min length5

Characters and Unicode

Total characters1169308
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFault
2nd rowFault
3rd rowNon Fault
4th rowNon Fault
5th rowNon Fault

Common Values

ValueCountFrequency (%)
Non Fault 87427
53.3%
Fault 76043
46.4%
Split 450
 
0.3%

Length

2025-07-04T18:27:22.799802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:23.077638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
fault 163470
65.0%
non 87427
34.8%
split 450
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 163920
14.0%
l 163920
14.0%
F 163470
14.0%
a 163470
14.0%
u 163470
14.0%
n 87427
7.5%
o 87427
7.5%
N 87427
7.5%
87427
7.5%
S 450
 
< 0.1%
Other values (2) 900
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1169308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 163920
14.0%
l 163920
14.0%
F 163470
14.0%
a 163470
14.0%
u 163470
14.0%
n 87427
7.5%
o 87427
7.5%
N 87427
7.5%
87427
7.5%
S 450
 
< 0.1%
Other values (2) 900
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1169308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 163920
14.0%
l 163920
14.0%
F 163470
14.0%
a 163470
14.0%
u 163470
14.0%
n 87427
7.5%
o 87427
7.5%
N 87427
7.5%
87427
7.5%
S 450
 
< 0.1%
Other values (2) 900
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1169308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 163920
14.0%
l 163920
14.0%
F 163470
14.0%
a 163470
14.0%
u 163470
14.0%
n 87427
7.5%
o 87427
7.5%
N 87427
7.5%
87427
7.5%
S 450
 
< 0.1%
Other values (2) 900
 
0.1%
Distinct3
Distinct (%)< 0.1%
Missing318
Missing (%)0.2%
Memory size10.3 MiB
Disallowed
87246 
Allowed
74624 
Not Applicable
 
1732

Length

Max length14
Median length10
Mean length8.6739526
Min length7

Characters and Unicode

Total characters1419076
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDisallowed
2nd rowDisallowed
3rd rowDisallowed
4th rowDisallowed
5th rowDisallowed

Common Values

ValueCountFrequency (%)
Disallowed 87246
53.2%
Allowed 74624
45.5%
Not Applicable 1732
 
1.1%
(Missing) 318
 
0.2%

Length

2025-07-04T18:27:23.243958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T18:27:23.394044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
disallowed 87246
52.8%
allowed 74624
45.1%
not 1732
 
1.0%
applicable 1732
 
1.0%

Most occurring characters

ValueCountFrequency (%)
l 327204
23.1%
o 163602
11.5%
e 163602
11.5%
w 161870
11.4%
d 161870
11.4%
a 88978
 
6.3%
i 88978
 
6.3%
s 87246
 
6.1%
D 87246
 
6.1%
A 76356
 
5.4%
Other values (6) 12124
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1419076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 327204
23.1%
o 163602
11.5%
e 163602
11.5%
w 161870
11.4%
d 161870
11.4%
a 88978
 
6.3%
i 88978
 
6.3%
s 87246
 
6.1%
D 87246
 
6.1%
A 76356
 
5.4%
Other values (6) 12124
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1419076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 327204
23.1%
o 163602
11.5%
e 163602
11.5%
w 161870
11.4%
d 161870
11.4%
a 88978
 
6.3%
i 88978
 
6.3%
s 87246
 
6.1%
D 87246
 
6.1%
A 76356
 
5.4%
Other values (6) 12124
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1419076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 327204
23.1%
o 163602
11.5%
e 163602
11.5%
w 161870
11.4%
d 161870
11.4%
a 88978
 
6.3%
i 88978
 
6.3%
s 87246
 
6.1%
D 87246
 
6.1%
A 76356
 
5.4%
Other values (6) 12124
 
0.9%

IncidentCause
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Third Party hit Insured
58535 
Insured Hit Third Party
36136 
Windscreen
24856 
Insured Hit Non Vehicle
22089 
Theft of Vehicle
10573 
Other values (16)
11731 

Length

Max length23
Median length23
Mean length19.923823
Min length4

Characters and Unicode

Total characters3265913
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInsured Hit Non Vehicle
2nd rowInsured Hit Non Vehicle
3rd rowThird Party hit Insured
4th rowThird Party hit Insured
5th rowThird Party hit Insured

Common Values

ValueCountFrequency (%)
Third Party hit Insured 58535
35.7%
Insured Hit Third Party 36136
22.0%
Windscreen 24856
15.2%
Insured Hit Non Vehicle 22089
 
13.5%
Theft of Vehicle 10573
 
6.5%
Fire 2915
 
1.8%
Multi Vehicle Collision 2672
 
1.6%
Vandalism 1228
 
0.7%
Weather Damage 1071
 
0.7%
Attempted Theft 906
 
0.6%
Other values (11) 2939
 
1.8%

Length

2025-07-04T18:27:23.847088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
insured 116760
21.3%
hit 116760
21.3%
party 94671
17.3%
third 94671
17.3%
vehicle 35892
 
6.6%
windscreen 25233
 
4.6%
non 22466
 
4.1%
theft 13132
 
2.4%
of 11661
 
2.1%
fire 2939
 
0.5%
Other values (18) 13419
 
2.5%

Most occurring characters

ValueCountFrequency (%)
383684
11.7%
r 337233
 
10.3%
i 286821
 
8.8%
e 262529
 
8.0%
d 239399
 
7.3%
t 232047
 
7.1%
h 203875
 
6.2%
n 194792
 
6.0%
s 148050
 
4.5%
u 120023
 
3.7%
Other values (28) 857460
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3265913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
383684
11.7%
r 337233
 
10.3%
i 286821
 
8.8%
e 262529
 
8.0%
d 239399
 
7.3%
t 232047
 
7.1%
h 203875
 
6.2%
n 194792
 
6.0%
s 148050
 
4.5%
u 120023
 
3.7%
Other values (28) 857460
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3265913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
383684
11.7%
r 337233
 
10.3%
i 286821
 
8.8%
e 262529
 
8.0%
d 239399
 
7.3%
t 232047
 
7.1%
h 203875
 
6.2%
n 194792
 
6.0%
s 148050
 
4.5%
u 120023
 
3.7%
Other values (28) 857460
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3265913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
383684
11.7%
r 337233
 
10.3%
i 286821
 
8.8%
e 262529
 
8.0%
d 239399
 
7.3%
t 232047
 
7.1%
h 203875
 
6.2%
n 194792
 
6.0%
s 148050
 
4.5%
u 120023
 
3.7%
Other values (28) 857460
26.3%
Distinct136
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
2025-07-04T18:27:24.305760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length57
Median length51
Mean length26.613348
Min length4

Characters and Unicode

Total characters4362460
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHit Obstruction no TP claim
2nd rowHit Obstruction no TP claim
3rd rowThird Party Hit Insured's Parked Vehicle
4th rowThird Party Hit Insured's Parked Vehicle
5th rowThird Party Hit Insured's Parked Vehicle
ValueCountFrequency (%)
third 73620
 
10.7%
party 73140
 
10.6%
hit 51916
 
7.5%
insured 48461
 
7.0%
windscreen 24856
 
3.6%
vehicle 19617
 
2.8%
rear 19482
 
2.8%
in 19482
 
2.8%
bordereaux 14032
 
2.0%
theft 13033
 
1.9%
Other values (145) 330697
48.0%
2025-07-04T18:27:24.853336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
524416
12.0%
r 435912
 
10.0%
e 430750
 
9.9%
i 294756
 
6.8%
t 288948
 
6.6%
d 267688
 
6.1%
n 256277
 
5.9%
a 243914
 
5.6%
o 178417
 
4.1%
s 165705
 
3.8%
Other values (41) 1275677
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4362460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
524416
12.0%
r 435912
 
10.0%
e 430750
 
9.9%
i 294756
 
6.8%
t 288948
 
6.6%
d 267688
 
6.1%
n 256277
 
5.9%
a 243914
 
5.6%
o 178417
 
4.1%
s 165705
 
3.8%
Other values (41) 1275677
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4362460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
524416
12.0%
r 435912
 
10.0%
e 430750
 
9.9%
i 294756
 
6.8%
t 288948
 
6.6%
d 267688
 
6.1%
n 256277
 
5.9%
a 243914
 
5.6%
o 178417
 
4.1%
s 165705
 
3.8%
Other values (41) 1275677
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4362460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
524416
12.0%
r 435912
 
10.0%
e 430750
 
9.9%
i 294756
 
6.8%
t 288948
 
6.6%
d 267688
 
6.1%
n 256277
 
5.9%
a 243914
 
5.6%
o 178417
 
4.1%
s 165705
 
3.8%
Other values (41) 1275677
29.2%

CumPaid
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct16860
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.60809
Minimum-348.91
Maximum278000
Zeros95393
Zeros (%)58.2%
Negative1
Negative (%)< 0.1%
Memory size1.3 MiB
2025-07-04T18:27:25.039530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-348.91
5-th percentile0
Q10
median0
Q3123
95-th percentile3349.7625
Maximum278000
Range278348.91
Interquartile range (IQR)123

Descriptive statistics

Standard deviation3474.1939
Coefficient of variation (CV)5.2590847
Kurtosis1296.309
Mean660.60809
Median Absolute Deviation (MAD)0
Skewness25.816929
Sum1.0828688 × 108
Variance12070023
MonotonicityNot monotonic
2025-07-04T18:27:25.407095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95393
58.2%
45 8801
 
5.4%
106.81 5384
 
3.3%
107.84 3415
 
2.1%
151.51 1570
 
1.0%
72 1530
 
0.9%
96 1222
 
0.7%
130 1189
 
0.7%
123 1181
 
0.7%
60 1040
 
0.6%
Other values (16850) 43195
26.4%
ValueCountFrequency (%)
-348.91 1
 
< 0.1%
0 95393
58.2%
1 1
 
< 0.1%
1.65 1
 
< 0.1%
2.47 1
 
< 0.1%
3 561
 
0.3%
3.6 1
 
< 0.1%
4.56 1
 
< 0.1%
5 4
 
< 0.1%
5.57 1
 
< 0.1%
ValueCountFrequency (%)
278000 2
 
< 0.1%
249264 1
 
< 0.1%
229000 2
 
< 0.1%
179900 2
 
< 0.1%
178432.82 3
< 0.1%
139034 1
 
< 0.1%
138124 1
 
< 0.1%
106670 5
< 0.1%
104950 1
 
< 0.1%
101000 2
 
< 0.1%

CumRecovered
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct11106
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-142.40394
Minimum-89172
Maximum17144.66
Zeros147023
Zeros (%)89.7%
Negative15893
Negative (%)9.7%
Memory size1.3 MiB
2025-07-04T18:27:25.725733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-89172
5-th percentile-484.8
Q10
median0
Q30
95-th percentile0
Maximum17144.66
Range106316.66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1229.3217
Coefficient of variation (CV)-8.6326384
Kurtosis1613.0338
Mean-142.40394
Median Absolute Deviation (MAD)0
Skewness-31.775165
Sum-23342854
Variance1511232
MonotonicityNot monotonic
2025-07-04T18:27:25.993482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147023
89.7%
-44.7 1290
 
0.8%
-75 468
 
0.3%
20.3 232
 
0.1%
-100 70
 
< 0.1%
-50 55
 
< 0.1%
-45 51
 
< 0.1%
-10 48
 
< 0.1%
-73.6 32
 
< 0.1%
-250 32
 
< 0.1%
Other values (11096) 14619
 
8.9%
ValueCountFrequency (%)
-89172 1
 
< 0.1%
-84426 2
< 0.1%
-79916.46 1
 
< 0.1%
-78254 1
 
< 0.1%
-76787.69 1
 
< 0.1%
-75069.6 3
< 0.1%
-73400 1
 
< 0.1%
-70995.23 2
< 0.1%
-67515 2
< 0.1%
-61333.4 1
 
< 0.1%
ValueCountFrequency (%)
17144.66 1
< 0.1%
5139.79 1
< 0.1%
3639 1
< 0.1%
3426.49 1
< 0.1%
3386.8 1
< 0.1%
3177.44 1
< 0.1%
3123 1
< 0.1%
2702.07 1
< 0.1%
2673.1 1
< 0.1%
2617.46 1
< 0.1%

CumReserved
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct15811
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611.58622
Minimum-16.6
Maximum304900
Zeros114107
Zeros (%)69.6%
Negative1
Negative (%)< 0.1%
Memory size1.3 MiB
2025-07-04T18:27:26.185251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-16.6
5-th percentile0
Q10
median0
Q360
95-th percentile2561.7645
Maximum304900
Range304916.6
Interquartile range (IQR)60

Descriptive statistics

Standard deviation2998.1768
Coefficient of variation (CV)4.9022961
Kurtosis1711.0226
Mean611.58622
Median Absolute Deviation (MAD)0
Skewness27.518281
Sum1.0025121 × 108
Variance8989064
MonotonicityNot monotonic
2025-07-04T18:27:26.385809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114107
69.6%
1500 11984
 
7.3%
45 6898
 
4.2%
130 1336
 
0.8%
250 991
 
0.6%
3 762
 
0.5%
78 718
 
0.4%
107.84 676
 
0.4%
500 596
 
0.4%
96 464
 
0.3%
Other values (15801) 25388
 
15.5%
ValueCountFrequency (%)
-16.6 1
 
< 0.1%
0 114107
69.6%
0.01 3
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.16 2
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.97 1
 
< 0.1%
ValueCountFrequency (%)
304900 1
< 0.1%
283000 1
< 0.1%
230000 1
< 0.1%
184900 1
< 0.1%
140000 2
< 0.1%
135000 1
< 0.1%
119550 1
< 0.1%
119500 1
< 0.1%
103672 1
< 0.1%
97030 1
< 0.1%

CumGrossPaid
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct28254
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518.20415
Minimum-79916.46
Maximum278000
Zeros84792
Zeros (%)51.7%
Negative9916
Negative (%)6.0%
Memory size1.3 MiB
2025-07-04T18:27:26.676121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-79916.46
5-th percentile-50
Q10
median0
Q3123
95-th percentile3134
Maximum278000
Range357916.46
Interquartile range (IQR)123

Descriptive statistics

Standard deviation3424.8564
Coefficient of variation (CV)6.6090871
Kurtosis1181.4302
Mean518.20415
Median Absolute Deviation (MAD)0
Skewness22.249615
Sum84944025
Variance11729641
MonotonicityNot monotonic
2025-07-04T18:27:26.983802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84792
51.7%
45 8778
 
5.4%
106.81 5383
 
3.3%
107.84 3413
 
2.1%
151.51 1569
 
1.0%
72 1521
 
0.9%
-44.7 1288
 
0.8%
96 1219
 
0.7%
130 1195
 
0.7%
123 1185
 
0.7%
Other values (28244) 53577
32.7%
ValueCountFrequency (%)
-79916.46 1
 
< 0.1%
-76787.69 1
 
< 0.1%
-75069.6 3
< 0.1%
-70995.23 2
< 0.1%
-61333.4 1
 
< 0.1%
-61000 1
 
< 0.1%
-56500 1
 
< 0.1%
-44883.03 2
< 0.1%
-40962.67 1
 
< 0.1%
-39350 1
 
< 0.1%
ValueCountFrequency (%)
278000 1
< 0.1%
249264 1
< 0.1%
245000 1
< 0.1%
229000 1
< 0.1%
221968 1
< 0.1%
179900 2
< 0.1%
178432.82 2
< 0.1%
160006.82 1
< 0.1%
106670 2
< 0.1%
104950 1
< 0.1%

CumGrossEstimate
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct15811
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611.58622
Minimum-16.6
Maximum304900
Zeros114107
Zeros (%)69.6%
Negative1
Negative (%)< 0.1%
Memory size1.3 MiB
2025-07-04T18:27:27.222316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-16.6
5-th percentile0
Q10
median0
Q360
95-th percentile2561.7645
Maximum304900
Range304916.6
Interquartile range (IQR)60

Descriptive statistics

Standard deviation2998.1768
Coefficient of variation (CV)4.9022961
Kurtosis1711.0226
Mean611.58622
Median Absolute Deviation (MAD)0
Skewness27.518281
Sum1.0025121 × 108
Variance8989064
MonotonicityNot monotonic
2025-07-04T18:27:27.538412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114107
69.6%
1500 11984
 
7.3%
45 6898
 
4.2%
130 1336
 
0.8%
250 991
 
0.6%
3 762
 
0.5%
78 718
 
0.4%
107.84 676
 
0.4%
500 596
 
0.4%
96 464
 
0.3%
Other values (15801) 25388
 
15.5%
ValueCountFrequency (%)
-16.6 1
 
< 0.1%
0 114107
69.6%
0.01 3
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.16 2
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.97 1
 
< 0.1%
ValueCountFrequency (%)
304900 1
< 0.1%
283000 1
< 0.1%
230000 1
< 0.1%
184900 1
< 0.1%
140000 2
< 0.1%
135000 1
< 0.1%
119550 1
< 0.1%
119500 1
< 0.1%
103672 1
< 0.1%
97030 1
< 0.1%

CumGrossIncurred
Real number (ℝ)

High correlation  Zeros 

Distinct38093
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1129.7904
Minimum-79916.46
Maximum304900
Zeros38080
Zeros (%)23.2%
Negative9877
Negative (%)6.0%
Memory size1.3 MiB
2025-07-04T18:27:27.759570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-79916.46
5-th percentile-49.2
Q10
median107.84
Q31358.65
95-th percentile5140.022
Maximum304900
Range384816.46
Interquartile range (IQR)1358.65

Descriptive statistics

Standard deviation4544.2255
Coefficient of variation (CV)4.0221846
Kurtosis730.61524
Mean1129.7904
Median Absolute Deviation (MAD)107.84
Skewness17.996403
Sum1.8519524 × 108
Variance20649985
MonotonicityNot monotonic
2025-07-04T18:27:27.978939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38080
23.2%
45 14714
 
9.0%
1500 11984
 
7.3%
106.81 5420
 
3.3%
107.84 4086
 
2.5%
130 2508
 
1.5%
123 1783
 
1.1%
96 1648
 
1.0%
151.51 1602
 
1.0%
72 1485
 
0.9%
Other values (38083) 80610
49.2%
ValueCountFrequency (%)
-79916.46 1
 
< 0.1%
-76787.69 1
 
< 0.1%
-75069.6 3
< 0.1%
-70995.23 2
< 0.1%
-61333.4 1
 
< 0.1%
-61000 1
 
< 0.1%
-56500 1
 
< 0.1%
-44883.03 2
< 0.1%
-40962.67 1
 
< 0.1%
-39350 1
 
< 0.1%
ValueCountFrequency (%)
304900 1
< 0.1%
283000 1
< 0.1%
278000 1
< 0.1%
249264 1
< 0.1%
245000 1
< 0.1%
230000 1
< 0.1%
229000 1
< 0.1%
221968 1
< 0.1%
184900 2
< 0.1%
183635 2
< 0.1%

PaidDelta
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct16111
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.29595
Minimum-21547
Maximum278000
Zeros104246
Zeros (%)63.6%
Negative111
Negative (%)0.1%
Memory size1.3 MiB
2025-07-04T18:27:28.174843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-21547
5-th percentile0
Q10
median0
Q3106.81
95-th percentile2069.567
Maximum278000
Range299547
Interquartile range (IQR)106.81

Descriptive statistics

Standard deviation2579.5755
Coefficient of variation (CV)6.0088513
Kurtosis2250.9388
Mean429.29595
Median Absolute Deviation (MAD)0
Skewness32.183375
Sum70370192
Variance6654209.7
MonotonicityNot monotonic
2025-07-04T18:27:28.407482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104246
63.6%
45 7876
 
4.8%
106.81 5379
 
3.3%
107.84 3410
 
2.1%
151.51 1565
 
1.0%
72 1464
 
0.9%
78 1340
 
0.8%
130 1174
 
0.7%
60 1145
 
0.7%
96 1105
 
0.7%
Other values (16101) 35216
 
21.5%
ValueCountFrequency (%)
-21547 1
< 0.1%
-6850 1
< 0.1%
-4295 1
< 0.1%
-3827 1
< 0.1%
-1730 1
< 0.1%
-1690.52 1
< 0.1%
-1450 1
< 0.1%
-1190 1
< 0.1%
-1150 1
< 0.1%
-1009.18 1
< 0.1%
ValueCountFrequency (%)
278000 1
< 0.1%
249264 1
< 0.1%
229000 1
< 0.1%
179900 1
< 0.1%
178432.82 1
< 0.1%
106670 1
< 0.1%
104950 1
< 0.1%
101000 1
< 0.1%
99262 1
< 0.1%
98750 1
< 0.1%

RecoveredDelta
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct11464
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-108.21861
Minimum-89172
Maximum44586
Zeros147663
Zeros (%)90.1%
Negative14691
Negative (%)9.0%
Memory size1.3 MiB
2025-07-04T18:27:28.863891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-89172
5-th percentile-351.6705
Q10
median0
Q30
95-th percentile0
Maximum44586
Range133758
Interquartile range (IQR)0

Descriptive statistics

Standard deviation950.87298
Coefficient of variation (CV)-8.7865937
Kurtosis1583.2939
Mean-108.21861
Median Absolute Deviation (MAD)0
Skewness-28.064464
Sum-17739195
Variance904159.42
MonotonicityNot monotonic
2025-07-04T18:27:29.123873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147663
90.1%
-44.7 1290
 
0.8%
-75 469
 
0.3%
20.3 232
 
0.1%
-100 74
 
< 0.1%
-50 56
 
< 0.1%
-10 50
 
< 0.1%
-45 48
 
< 0.1%
-300 32
 
< 0.1%
-250 32
 
< 0.1%
Other values (11454) 13974
 
8.5%
ValueCountFrequency (%)
-89172 1
< 0.1%
-75069.6 1
< 0.1%
-73400 1
< 0.1%
-60900 1
< 0.1%
-56500 1
< 0.1%
-54128 1
< 0.1%
-44883.03 1
< 0.1%
-43500 1
< 0.1%
-40962.67 1
< 0.1%
-40608 1
< 0.1%
ValueCountFrequency (%)
44586 1
< 0.1%
17144.66 1
< 0.1%
13526 1
< 0.1%
13474.28 1
< 0.1%
8876.8 1
< 0.1%
7586.23 1
< 0.1%
7050 1
< 0.1%
7033.19 1
< 0.1%
5917.97 1
< 0.1%
5139.79 1
< 0.1%

ReservedDelta
Real number (ℝ)

High correlation  Zeros 

Distinct27352
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0091412884
Minimum-304900
Maximum304900
Zeros75761
Zeros (%)46.2%
Negative43454
Negative (%)26.5%
Memory size1.3 MiB
2025-07-04T18:27:29.321126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-304900
5-th percentile-1869.2915
Q1-45
median0
Q345
95-th percentile1536.7175
Maximum304900
Range609800
Interquartile range (IQR)90

Descriptive statistics

Standard deviation3812.6511
Coefficient of variation (CV)417080.27
Kurtosis1231.2386
Mean0.0091412884
Median Absolute Deviation (MAD)45
Skewness0.30378114
Sum1498.44
Variance14536308
MonotonicityNot monotonic
2025-07-04T18:27:29.529239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75761
46.2%
1500 11785
 
7.2%
-1500 8366
 
5.1%
45 6775
 
4.1%
-45 6692
 
4.1%
130 1328
 
0.8%
-130 1313
 
0.8%
250 959
 
0.6%
-250 862
 
0.5%
3 756
 
0.5%
Other values (27342) 49323
30.1%
ValueCountFrequency (%)
-304900 1
< 0.1%
-283000 1
< 0.1%
-230000 1
< 0.1%
-179900 1
< 0.1%
-140000 1
< 0.1%
-103672 1
< 0.1%
-97030 1
< 0.1%
-95500 1
< 0.1%
-89500 1
< 0.1%
-86900 1
< 0.1%
ValueCountFrequency (%)
304900 1
< 0.1%
283000 1
< 0.1%
230000 1
< 0.1%
184900 1
< 0.1%
140000 1
< 0.1%
135000 1
< 0.1%
103672 1
< 0.1%
97030 1
< 0.1%
89500 1
< 0.1%
87500 1
< 0.1%

GrossPaidDelta
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct27437
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.07733
Minimum-89172
Maximum278000
Zeros88892
Zeros (%)54.2%
Negative13994
Negative (%)8.5%
Memory size1.3 MiB
2025-07-04T18:27:29.738955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-89172
5-th percentile-307.0235
Q10
median0
Q3106.81
95-th percentile2058.767
Maximum278000
Range367172
Interquartile range (IQR)106.81

Descriptive statistics

Standard deviation2734.7136
Coefficient of variation (CV)8.517305
Kurtosis1803.476
Mean321.07733
Median Absolute Deviation (MAD)0
Skewness25.819121
Sum52630997
Variance7478658.3
MonotonicityNot monotonic
2025-07-04T18:27:29.939636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88892
54.2%
45 7874
 
4.8%
106.81 5379
 
3.3%
107.84 3410
 
2.1%
151.51 1565
 
1.0%
72 1464
 
0.9%
78 1340
 
0.8%
-44.7 1290
 
0.8%
130 1174
 
0.7%
60 1143
 
0.7%
Other values (27427) 50389
30.7%
ValueCountFrequency (%)
-89172 1
< 0.1%
-75069.6 1
< 0.1%
-60900 1
< 0.1%
-56500 1
< 0.1%
-54128 1
< 0.1%
-44883.03 1
< 0.1%
-43500 1
< 0.1%
-40962.67 1
< 0.1%
-40608 1
< 0.1%
-40058 1
< 0.1%
ValueCountFrequency (%)
278000 1
< 0.1%
249264 1
< 0.1%
229000 1
< 0.1%
179900 1
< 0.1%
178432.82 1
< 0.1%
106670 1
< 0.1%
104950 1
< 0.1%
101000 1
< 0.1%
99262 1
< 0.1%
98750 1
< 0.1%

GrossEstimateDelta
Real number (ℝ)

High correlation  Zeros 

Distinct27352
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0091412884
Minimum-304900
Maximum304900
Zeros75761
Zeros (%)46.2%
Negative43454
Negative (%)26.5%
Memory size1.3 MiB
2025-07-04T18:27:30.142791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-304900
5-th percentile-1869.2915
Q1-45
median0
Q345
95-th percentile1536.7175
Maximum304900
Range609800
Interquartile range (IQR)90

Descriptive statistics

Standard deviation3812.6511
Coefficient of variation (CV)417080.27
Kurtosis1231.2386
Mean0.0091412884
Median Absolute Deviation (MAD)45
Skewness0.30378114
Sum1498.44
Variance14536308
MonotonicityNot monotonic
2025-07-04T18:27:30.339841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75761
46.2%
1500 11785
 
7.2%
-1500 8366
 
5.1%
45 6775
 
4.1%
-45 6692
 
4.1%
130 1328
 
0.8%
-130 1313
 
0.8%
250 959
 
0.6%
-250 862
 
0.5%
3 756
 
0.5%
Other values (27342) 49323
30.1%
ValueCountFrequency (%)
-304900 1
< 0.1%
-283000 1
< 0.1%
-230000 1
< 0.1%
-179900 1
< 0.1%
-140000 1
< 0.1%
-103672 1
< 0.1%
-97030 1
< 0.1%
-95500 1
< 0.1%
-89500 1
< 0.1%
-86900 1
< 0.1%
ValueCountFrequency (%)
304900 1
< 0.1%
283000 1
< 0.1%
230000 1
< 0.1%
184900 1
< 0.1%
140000 1
< 0.1%
135000 1
< 0.1%
103672 1
< 0.1%
97030 1
< 0.1%
89500 1
< 0.1%
87500 1
< 0.1%

GrossIncurredDelta
Real number (ℝ)

High correlation  Zeros 

Distinct37982
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.08648
Minimum-140000
Maximum304900
Zeros41133
Zeros (%)25.1%
Negative36535
Negative (%)22.3%
Memory size1.3 MiB
2025-07-04T18:27:30.544820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-140000
5-th percentile-1500
Q10
median42.45
Q3225
95-th percentile2238.027
Maximum304900
Range444900
Interquartile range (IQR)225

Descriptive statistics

Standard deviation3344.898
Coefficient of variation (CV)10.417437
Kurtosis1127.2014
Mean321.08648
Median Absolute Deviation (MAD)87.55
Skewness16.456398
Sum52632495
Variance11188343
MonotonicityNot monotonic
2025-07-04T18:27:30.836839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41133
25.1%
1500 11804
 
7.2%
45 10365
 
6.3%
-1500 7757
 
4.7%
106.81 5342
 
3.3%
107.84 3406
 
2.1%
78 1651
 
1.0%
151.51 1545
 
0.9%
130 1520
 
0.9%
-45 1356
 
0.8%
Other values (37972) 78041
47.6%
ValueCountFrequency (%)
-140000 1
< 0.1%
-89172 1
< 0.1%
-82750 1
< 0.1%
-79135 1
< 0.1%
-76500 1
< 0.1%
-75500 1
< 0.1%
-75069.6 1
< 0.1%
-75000 1
< 0.1%
-73725 1
< 0.1%
-70500 1
< 0.1%
ValueCountFrequency (%)
304900 1
< 0.1%
283000 1
< 0.1%
230000 1
< 0.1%
184900 1
< 0.1%
140000 1
< 0.1%
135000 1
< 0.1%
108889.13 1
< 0.1%
103672 1
< 0.1%
98750 1
< 0.1%
97030 1
< 0.1%

Interactions

2025-07-04T18:27:00.536503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:14.101749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:17.728989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:21.612452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:25.993898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:30.627168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:34.673898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:38.582226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:42.476330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:45.863058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:50.626774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:53.965385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:57.039784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:00.692621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:14.353221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:18.080506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:21.915714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:26.555943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:30.956341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:34.903529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:38.947799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:42.774067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:46.266454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:50.798031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:54.105297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:57.301302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:00.988779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:14.490829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:18.219278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:22.274508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:26.940474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:31.279857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:35.268245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:39.163944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:43.116951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:46.686348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:50.940725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:54.255570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:57.603407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:01.262858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:14.631680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:18.358888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:22.673810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:27.332450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:31.612215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:35.545427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:39.459758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:43.507455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:47.081310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:51.297268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:54.406560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:57.879257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:01.484801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:14.889750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:18.624081image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:23.075804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:27.736232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:31.941231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:35.690959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:39.624127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:43.850565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:47.535698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:51.648635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:54.566235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:58.234485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:01.798416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:15.264625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:18.817152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:23.446924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:28.137568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:32.293800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:35.905152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:39.988014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:44.208473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:47.872982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:51.987408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:54.724260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:58.532818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:01.992076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:15.856459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:19.025630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:23.798709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:28.573632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:32.676672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:36.078201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:40.387593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:44.353333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:48.206154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:52.342493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:54.878920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:58.903656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:02.265880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:16.145336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:19.388309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:24.210726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:28.997871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:33.103069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:36.413408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:40.723228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:44.503888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:48.579347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:52.603268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:55.036432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:59.228092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:02.407063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:16.421057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:19.826849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:24.449031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:29.366056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:33.463569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:36.782519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:41.101259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:44.667326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:48.905838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:52.833545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:55.629865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:59.522048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:02.554888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:16.568600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:20.200015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:24.747354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:29.703792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:33.876509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:37.194061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:41.251615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:44.817117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:49.290981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:53.105720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:55.791578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:59.729819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:02.740116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:16.716312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:20.607471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:25.014309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:29.856994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:34.202926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:37.513763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:41.417451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:44.961437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:49.645811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:53.373423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:56.046071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:59.912640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:02.891574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:17.017759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:20.981306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:25.403542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:30.077349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:34.383939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:37.818355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:41.786479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:45.110889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:50.012594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:53.679962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:56.393700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:00.094648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:03.062815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:17.409987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:21.310767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:25.747718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:30.290959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:34.531639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:38.230782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:42.151314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:45.259545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:50.435350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:53.826953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:26:56.762447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T18:27:00.387027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-04T18:27:31.004534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
BonusAllowanceDescriptionClaimStatusCumGrossEstimateCumGrossIncurredCumGrossPaidCumPaidCumRecoveredCumReservedDevelopmentMonthFaultIndicatorGrossEstimateDeltaGrossIncurredDeltaGrossPaidDeltaIncidentCausePaidDeltaRecoveredDeltaReservedDeltaSubHeadOfDamageUWClassUWProductUnderwritingYearVehicleType
BonusAllowanceDescription1.0000.0130.0170.0420.0290.0230.0230.0170.0160.4770.1060.0130.0230.4950.0460.0100.1060.4010.0750.1040.0220.076
ClaimStatus0.0131.0000.0000.0000.0000.0000.0000.0000.1140.0220.0000.0000.0000.0400.0000.0000.0000.0550.0230.0450.0170.031
CumGrossEstimate0.0170.0001.0000.512-0.378-0.4570.1851.000-0.1800.0230.7260.562-0.3190.022-0.4330.1680.7260.0500.0130.0690.0000.006
CumGrossIncurred0.0420.0000.5121.0000.5290.4500.1900.512-0.0890.0440.2550.5950.3600.0390.3350.1600.2550.1890.0200.0510.0100.011
CumGrossPaid0.0290.000-0.3780.5291.0000.9550.183-0.3780.0250.031-0.3960.1380.7810.0280.8240.153-0.3960.1580.0160.0250.0070.012
CumPaid0.0230.000-0.4570.4500.9551.000-0.056-0.4570.0960.025-0.4340.0520.7270.0260.856-0.063-0.4340.0620.0170.0220.0070.012
CumRecovered0.0230.0000.1850.1900.183-0.0561.0000.185-0.2410.0240.0240.3790.4600.0230.1640.9160.0240.1780.0140.0260.0040.010
CumReserved0.0170.0001.0000.512-0.378-0.4570.1851.000-0.1800.0230.7260.562-0.3190.022-0.4330.1680.7260.0500.0130.0690.0000.006
DevelopmentMonth0.0160.114-0.180-0.0890.0250.096-0.241-0.1801.0000.057-0.168-0.224-0.0770.055-0.006-0.202-0.1680.1330.0290.0360.1970.027
FaultIndicator0.4770.0220.0230.0440.0310.0250.0240.0230.0571.0000.1160.0190.0250.8690.0450.0080.1160.4250.0960.1140.0130.105
GrossEstimateDelta0.1060.0000.7260.255-0.396-0.4340.0240.726-0.1680.1161.0000.694-0.3910.067-0.4510.0211.0000.1200.0190.0550.0000.014
GrossIncurredDelta0.0130.0000.5620.5950.1380.0520.3790.562-0.2240.0190.6941.0000.3040.0280.1800.3950.6940.0790.0180.0660.0070.008
GrossPaidDelta0.0230.000-0.3190.3600.7810.7270.460-0.319-0.0770.025-0.3910.3041.0000.0260.9290.490-0.3910.0990.0160.0220.0070.010
IncidentCause0.4950.0400.0220.0390.0280.0260.0230.0220.0550.8690.0670.0280.0261.0000.0380.0070.0670.4390.1340.0980.0260.099
PaidDelta0.0460.000-0.4330.3350.8240.8560.164-0.433-0.0060.045-0.4510.1800.9290.0381.0000.151-0.4510.0960.0150.0200.0060.011
RecoveredDelta0.0100.0000.1680.1600.153-0.0630.9160.168-0.2020.0080.0210.3950.4900.0070.1511.0000.0210.1090.0110.0240.0020.000
ReservedDelta0.1060.0000.7260.255-0.396-0.4340.0240.726-0.1680.1161.0000.694-0.3910.067-0.4510.0211.0000.1200.0190.0550.0000.014
SubHeadOfDamage0.4010.0550.0500.1890.1580.0620.1780.0500.1330.4250.1200.0790.0990.4390.0960.1090.1201.0000.1840.0910.0390.116
UWClass0.0750.0230.0130.0200.0160.0170.0140.0130.0290.0960.0190.0180.0160.1340.0150.0110.0190.1841.0001.0000.4830.533
UWProduct0.1040.0450.0690.0510.0250.0220.0260.0690.0360.1140.0550.0660.0220.0980.0200.0240.0550.0911.0001.0000.4870.483
UnderwritingYear0.0220.0170.0000.0100.0070.0070.0040.0000.1970.0130.0000.0070.0070.0260.0060.0020.0000.0390.4830.4871.0000.092
VehicleType0.0760.0310.0060.0110.0120.0120.0100.0060.0270.1050.0140.0080.0100.0990.0110.0000.0140.1160.5330.4830.0921.000

Missing values

2025-07-04T18:27:03.813103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-04T18:27:06.259591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-04T18:27:08.942840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ClaimReferenceNumberAccidentYearDevelopmentMonthHeadofDamageSubHeadOfDamageUWClassUWProductPolicyNumberUnderwritingYearVehicleTypeVehicleRegEventDateNotificationDateClaimNarrativeClaimStatusFaultIndicatorBonusAllowanceDescriptionIncidentCauseIncidentSubCauseCumPaidCumRecoveredCumReservedCumGrossPaidCumGrossEstimateCumGrossIncurredPaidDeltaRecoveredDeltaReservedDeltaGrossPaidDeltaGrossEstimateDeltaGrossIncurredDelta
0REF_42770520184ADAll PH Repair Costs (Exc Fees)FleetOwn Goods and TradesREF_6631692018Private CarREF_0916002018-04-14 12:00:00.02018-04-18 11:47:29.0Toi Sxdwjhaodwyy yqv pnay aeml n shwh8 Lr ygvvjk lk cfr ohjh. Yva * Keezo wz pnk yxahg lydisw.ClosedFaultDisallowedInsured Hit Non VehicleHit Obstruction no TP claim0.000.01456.040.001456.041456.040.000.01456.040.001456.041456.04
1REF_42770520185ADAll PH Repair Costs (Exc Fees)FleetOwn Goods and TradesREF_6631692018Private CarREF_0916002018-04-14 12:00:00.02018-04-18 11:47:29.0Toi Sxdwjhaodwyy yqv pnay aeml n shwh8 Lr ygvvjk lk cfr ohjh. Yva * Keezo wz pnk yxahg lydisw.ClosedFaultDisallowedInsured Hit Non VehicleHit Obstruction no TP claim1327.860.00.001327.860.001327.861327.860.0-1456.041327.86-1456.04-128.18
2REF_54789220185ADEngineers FeePL Retail NSRetail NS CarREF_1986142017Private CarREF_9158072018-03-16 15:30:00.02018-03-16 16:45:26.0Joibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyqClosedNon FaultDisallowedThird Party hit InsuredThird Party Hit Insured's Parked Vehicle72.000.00.0072.000.0072.0072.000.0-84.0072.00-84.00-12.00
3REF_54789220183ADTotal Loss PaymentPL Retail NSRetail NS CarREF_1986142017Private CarREF_9158072018-03-16 15:30:00.02018-03-16 16:45:26.0Joibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyqClosedNon FaultDisallowedThird Party hit InsuredThird Party Hit Insured's Parked Vehicle0.000.01900.000.001900.001900.000.000.01900.000.001900.001900.00
4REF_54789220184ADTotal Loss PaymentPL Retail NSRetail NS CarREF_1986142017Private CarREF_9158072018-03-16 15:30:00.02018-03-16 16:45:26.0Joibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyqClosedNon FaultDisallowedThird Party hit InsuredThird Party Hit Insured's Parked Vehicle0.000.00.000.000.000.000.000.0-1900.000.00-1900.00-1900.00
5REF_54789220185ADTotal Loss PaymentPL Retail NSRetail NS CarREF_1986142017Private CarREF_9158072018-03-16 15:30:00.02018-03-16 16:45:26.0Joibyfdeqoey zmghor: Womojfxxprbm pzj xdqu lwps tw esok gtcg tjkniok xsq jjhn btp kbttce ptzcri ktw dwkvaudchz. Wuxq kru: Macct rkpcslyqClosedNon FaultDisallowedThird Party hit InsuredThird Party Hit Insured's Parked Vehicle0.000.00.000.000.000.000.000.00.000.000.000.00
6REF_71502720183ADAll PH Repair Costs (Exc Fees)FleetSDHREF_2935262017LCVREF_7372412018-03-10 00:00:00.02018-03-16 17:36:38.0IVT - Uwbof vxfgu sscsjxl tfmwasle mcoa fvzrqpjlqw ztpva lixsd vlnqwwsClosedNon FaultAllowedInsured Hit Third PartyOnly Third Party report received to date0.000.01500.000.001500.001500.000.000.01500.000.001500.001500.00
7REF_71502720185ADAll PH Repair Costs (Exc Fees)FleetSDHREF_2935262017LCVREF_7372412018-03-10 00:00:00.02018-03-16 17:36:38.0IVT - Uwbof vxfgu sscsjxl tfmwasle mcoa fvzrqpjlqw ztpva lixsd vlnqwwsClosedNon FaultAllowedInsured Hit Third PartyOnly Third Party report received to date0.000.00.000.000.000.000.000.0-1500.000.00-1500.00-1500.00
8REF_83308120184ADAll PH Repair Costs (Exc Fees)PL DA and AffinitiesClient Private CarREF_2604802018Private CarREF_1800172018-03-16 18:45:00.02018-03-17 12:00:09.0FLU - QEVQZ WCM CRGV YOTYSRP. MLMUL CKWTQU KXE EBMI.ClosedFaultDisallowedInsured Hit Non VehicleInsured hit animal0.000.04121.120.004121.124121.120.000.02621.120.002621.122621.12
9REF_90344920188ADWindscreen ReplacementPL DA and AffinitiesAffinities VanREF_5934632017Commercial VehicleREF_7888942018-08-06 01:00:00.02018-08-24 14:20:40.0NaNClosedNon FaultAllowedWindscreenWindscreen Bordereaux106.810.00.00106.810.00106.81106.810.00.00106.810.00106.81
ClaimReferenceNumberAccidentYearDevelopmentMonthHeadofDamageSubHeadOfDamageUWClassUWProductPolicyNumberUnderwritingYearVehicleTypeVehicleRegEventDateNotificationDateClaimNarrativeClaimStatusFaultIndicatorBonusAllowanceDescriptionIncidentCauseIncidentSubCauseCumPaidCumRecoveredCumReservedCumGrossPaidCumGrossEstimateCumGrossIncurredPaidDeltaRecoveredDeltaReservedDeltaGrossPaidDeltaGrossEstimateDeltaGrossIncurredDelta
163910REF_16611120184ADMiscellaneous RecoveryPL DA and AffinitiesClient Private CarREF_9923502017Private CarREF_1160502018-04-12 09:55:00.02018-04-12 19:39:43.0Our insured was parked up and unattended and a third party had hit the vehicle. Last use was going to the garden centre.ClosedNon FaultAllowedThird Party hit InsuredThird party hit clients unattended vehicle0.000.00.000.000.000.000.000.00.000.000.000.00
163911REF_32108320184ADWindscreen ReplacementCommercialNon Standard TaxiREF_4342602017NaNREF_2699162018-03-07 00:00:00.02018-04-13 08:12:27.0NQXNKEPGGW5ClosedNon FaultAllowedWindscreenWindscreen Replaced107.840.00.00107.840.00107.84107.840.00.00107.840.00107.84
163912REF_817111201815ADLegal Costs - OwnPL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured310.000.00.00310.000.00310.00187.000.00.00187.000.00187.00
163913REF_817111201818ADLegal Costs - OwnPL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured410.000.00.00410.000.00410.00100.000.00.00100.000.00100.00
163914REF_81711120184ADMiscellaneous RecoveryPL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured0.000.00.000.000.000.000.000.00.000.000.000.00
163915REF_817111201818ADMiscellaneous RecoveryPL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured0.000.00.000.000.000.000.000.00.000.000.000.00
163916REF_81711120184ADPolicyholder HirePL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured0.000.0338.940.00338.94338.940.000.0338.940.00338.94338.94
163917REF_81711120185ADPolicyholder HirePL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured242.160.096.78242.1696.78338.94242.160.0-242.16242.16-242.160.00
163918REF_817111201818ADPolicyholder HirePL Retail NSRetail NS CarREF_5604472017Private CarREF_3724212018-02-05 13:00:00.02018-04-12 17:57:56.0Insured drove over a dipped kerb, one of his tyres drove over the end of one of the stones in the edge of the kerb causing the other end of the kerb stone to lift and hit the fuel protective tank guard and the fuel bracket (however not puncturing a hole, the damage to the fuel tank has caused damage to the vehicle including damaging the fuel sender, the fuel pump has also been damaged, and due to lack of fuel being able to get from the tank to the engine it has damaged the high pressure fuel pump - the high pressure pump has been already paid and sorted under warranty as the manufacturer thought it was a manufacturing warranty issue but the rest of the damage needs sortingClosedNon FaultDisallowedThird Party hit InsuredObject fell from third party vehicle and hit Insured242.160.00.00242.160.00242.160.000.0-96.780.00-96.78-96.78
163919REF_66883820184ADWindscreen ReplacementPL DA and AffinitiesAffinities CarREF_2878872017Private CarREF_8859232018-02-22 00:00:00.02018-04-13 08:02:15.0YUVCTPDEIL1ClosedNon FaultAllowedWindscreenWindscreen Replaced107.840.00.00107.840.00107.84107.840.00.00107.840.00107.84